Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 2
415-420,437

Federated learning evolutionary algorithm based on multi-objective optimization

Hu Zhiyonga
Yu Qianchenga,b
Wang Zhicia
Zhang Lisia
a. School of Computer Science & Engineering, b. The Key Laboratory of Images & Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan 750021, China
DOI: 10.19734/j.issn.1001-3695.2023.05.0235

Abstract

Traditional federated learning faces challenges such as high communication costs, structural heterogeneity, and insufficient privacy protection. To address these issues, this paper proposed a federated learning evolutionary algorithm. It applied sparse evolutionary training algorithm to reduce communication costs and integrated local differential privacy protection for participants' privacy. Additionally, it utilized the NSGA-Ⅲ algorithm to optimize the network structure and sparsity of the global federated learning model, adjusting the relationship between data availability and privacy protection. This achieved a balance between the effectiveness, communication costs, and privacy of the global federated learning model. Experimental results under unstable communication environments demonstrate that, on the MNIST and CIFAR-10 datasets, compared to the solution with the lowest error rate using the FNSGA-Ⅲ algorithm, the proposed algorithm improves communication efficiency by 57.19% and 52.17%, respectively. The participants also achieve(3.46, 10-4) and(6.52, 10-4) -local differential privacy. This algorithm can effectively reduce communication costs and protect participant privacy without significantly compromising the accuracy of the global model.

Foundation Support

宁夏重点研发计划(引才专项)项目(2022YCZX0013)
宁夏重点研发计划(重点)项目(2023BDE02001)
银川市校企联合创新项目(2022XQZD009)
北方民族大学2022年校级科研平台《数字化农业赋能宁夏乡村振兴创新团队》项目(2022PT_S10)
“图像与智能信息处理创新团队”国家民委创新团队资助项目

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.05.0235
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 2
Section: Algorithm Research & Explore
Pages: 415-420,437
Serial Number: 1001-3695(2024)02-014-0415-06

Publish History

[2023-08-03] Accepted Paper
[2024-02-05] Printed Article

Cite This Article

胡智勇, 于千城, 王之赐, 等. 基于多目标优化的联邦学习进化算法 [J]. 计算机应用研究, 2024, 41 (2): 415-420,437. (Hu Zhiyong, Yu Qiancheng, Wang Zhici, et al. Federated learning evolutionary algorithm based on multi-objective optimization [J]. Application Research of Computers, 2024, 41 (2): 415-420,437. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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